Why AI Coding Assistants Keep Resetting Like Amnesiacs

Hey, if you've ever teamed up with an AI to tackle some coding, you know the drill all too well – it's like starting a conversation from scratch every single time.

Session 1 (Copilot): "Build the auth module with OAuth support"
  → Great work. Deep debugging. Important decisions made.

Session 2 (Claude): "Can you continue the auth work?"
  → "What auth module? I don't see any context about OAuth."

Session 3 (Cursor): "We need to refactor the auth flow"
  → "Can you explain the project architecture first?"

Each fresh interaction means diving back into the basics: describing your setup, reminding it of past choices, and burning through resources just to rebuild what should stick around. And when you're collaborating with others? That headache multiplies.

  • A colleague's AI starts clueless about your progress
  • Those specs locked in PDFs or Word files? Totally invisible to the AI since it skips binary stuff
  • Key details get lost in a mess of chat threads, notes, and casual talks

Frustrated by this loop, I decided to whip up something called fcontext – check it out here.

Unpacking fcontext's Core Idea

Picture fcontext as this handy command-line buddy that's free and open-source. It sets up a special folder named .fcontext/ right in your project's root, acting like a collective memory bank that any AI coding assistant taps into at the beginning of a session.

pip install fcontext

Getting it rolling is super straightforward with just one line:

cd your-project
fcontext init
fcontext enable copilot   # or: claude, cursor, trae

Boom – now your AI pulls in all that essential background without you lifting a finger.

The Mechanics Behind the Magic

Inside that .fcontext/ spot, you'll find a bunch of organized goodies:

your-project/
  .fcontext/
    _README.md          # AI-maintained project summary
    _workspace.map      # Auto-generated project structure
    _cache/             # Binary docs converted to Markdown
    _topics/            # Session knowledge & conclusions
    _requirements/      # Stories, tasks, bugs
    _experiences/       # Imported team knowledge (read-only)

Every type of AI gets tailored guidance in the format it understands best:

Agent Config Location GitHub Copilot .github/instructions/*.instructions.md Claude Code .claude/rules/*.md Cursor .cursor/rules/*.md Trae .trae/rules/*.md

These guidelines nudge the AI to follow a smart routine:

  1. Kick off by scanning _README.md for the big picture on your work
  2. Dig into _topics/ to recall outcomes from earlier chats
  3. Peek at _cache/ instead of bugging you about non-text files
  4. Lean on fcontext req for pulling in requirements smoothly
  5. Stash key takeaways in _topics/ as the interaction wraps up

Highlight: Building Memory That Spans Interactions

This is where it really shines – the AI stashes insights from one chat into _topics/, so the next one jumps right in without missing a beat.

# End of today's session — AI saves conclusions
# .fcontext/_topics/auth-debugging.md gets created automatically

# Tomorrow, new session:
# AI reads _topics/ → knows exactly what happened yesterday

# You can also check manually:
fcontext topic list
fcontext topic show auth-debugging

Old school: "Hey, what's this all about?" With fcontext: "Building on that OAuth fix from last time, shall we tackle the GitHub integration next?"

Highlight: Seamless Swaps Between Different AI Tools

Feel free to hop from one assistant to another; they all draw from the same .fcontext/ wellspring.

fcontext enable copilot
fcontext enable claude
fcontext enable cursor

# All three agents now share the same context
# Use Cursor for frontend, Claude for backend — no context loss

No getting stuck with one provider – this knowledge stays tied to your project, keeping things flexible and independent.

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